Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Statistics & Probabilistic Programming in Julia
Basic Statistics
-
Statistics
- Summary Statistics using the statistics package
-
Distributions & StatsBase package
- Univariate & multivariate distributions
- Moments
- Probability functions
- Sampling and RNG
- Histograms
- Maximum likelihood estimation
- Product, truncation, and censored distributions
- Robust statistics
- Correlation & covariance
DataFrames
(DataFrames package)
- Data I/O
- Creating Data Frames
- Data types, including categorical and missing data
- Sorting & joining
- Reshaping & pivoting data
Hypothesis Testing
(HypothesisTests package)
- Principles of hypothesis testing
- Chi-Squared test
- z-test and t-test
- F-test
- Fisher exact test
- ANOVA
- Tests for normality
- Kolmogorov-Smirnov test
- Hotelling's T-test
Regression & Survival Analysis
(GLM & Survival packages)
- Principles of linear regression and exponential family
- Linear regression
-
Generalized linear models
- Logistic regression
- Poisson regression
- Gamma regression
- Other GLM models
-
Survival analysis
- Events
- Kaplan-Meier
- Nelson-Aalen
- Cox Proportional Hazard
Distances
(Distances package)
- What is a distance?
- Euclidean
- Cityblock
- Cosine
- Correlation
- Mahalanobis
- Hamming
- MAD
- RMS
- Mean squared deviation
Multivariate Statistics
(MultivariateStats, Lasso, & Loess packages)
- Ridge regression
- Lasso regression
- Loess
- Linear discriminant analysis
-
Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent Component Analysis
- Principal Component Regression (PCR)
- Factor Analysis
- Canonical Correlation Analysis
- Multidimensional scaling
Clustering
(Clustering package)
- K-means
- K-medoids
- DBSCAN
- Hierarchical clustering
- Markov Cluster Algorithm
- Fuzzy C-means clustering
Bayesian Statistics & Probabilistic Programming
(Turing package)
- Markov Chain Monte Carlo
- Hamiltonian Monte Carlo
- Gaussian Mixture Models
- Bayesian Linear Regression
- Bayesian Exponential Family Regression
- Bayesian Neural Networks
- Hidden Markov Models
- Particle Filtering
-
Variational Inference
Requirements
This course is intended for individuals who already have a background in data science and statistics.
21 Hours
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
